Answer:
it is 8/5
Step-by-step explanation:
Answer:
The model does not fits the data well.
Step-by-step explanation:
Correlation:
- Correlation is a technique that help us to find or define a relationship between two variables.
- It is a measure of linear relationship between two quantities.
- A positive correlation means that an increase in one quantity leads to an increase in another quantity
- A negative correlation means with increase in one quantity the other quantity decreases.
R-square, 
- The quantity R-squared is an indicator of the predictive power of a model.
- It explains the variation in the dependent variable due to independent variable.
- It shows how well the model fits the data.
- R-squared is also known as the coefficient of determination.

Therefore, only 36% of the variations in the dependent variable is explained by the independent variable in the model which means more than 50% of variation cannot still be explained in the dependent variable.
Hence, the model does not fits the data well.
Answer:
When you have two sets of data, one that represents an independent variable (X) and another that represents a dependent variable (Y) (it is a response of the first variable). A diagram called scatterplot can be generated to present the ordered pairs (X, Y) in the Cartesian plane to see how the two variables are related.
In this case the independent variable (X) is the age variable of the calf and the dependent variable (Y) is the weight of the calf.
The diagram can be seen in the attached file.
Step-by-step explanation:
Answer:
I think the width would be 150 yd
Step-by-step explanation:
just broke down the 210 yd we had already to inches found out how much it was scaled up and did the same on the other side
Answer:
50:20:50
This is because you add 5+2+5=12
120/12=10
5x10=50 2x10=20 5x10=50